{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "level": 1,
   "source": [
    "This is our data exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 50\n",
    "x = np.random.random(N)\n",
    "y = np.random.random(N)\n",
    "colors = np.random.random(N)\n",
    "\n",
    "area = np.pi * (15 * np.random.random(N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119db4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "surf = plt.scatter(x, y, s=area, alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}